Introduction

In this exercise we want to analyse the impact of COVID-19 on vessel densities in the Belgian Part of the North Sea and the Western Scheldt. There are different ways to calculate vessel densities, therefore we first start with some background information on two different types of vessel densities and where this data can be found. The data and code of this analysis are freely available, see the ‘Data availability’ and ‘Code availability’ sections at the end of this document. This analysis is used in following policy information note (BIN) from Flanders Marine Institute: http://www.vliz.be/nl/catalogus?module=ref&refid=324473

Vessel densities

In this small exercise we want to analyse vessel route densities in the Belgian part of the North Sea. The data for this exercise comes from EMODnet Human activities. EMODnet Human Activities has two types of vessel density data, one created by the Human Activities portal themselves, giving the vessel hours per square km per month by ship type. See here.

And one created by the European Maritime Safety Agency (EMSA), giving the number of routes per square km per month) by ship type. The advantage of the latter is that this provides recent information. For example, writing now 28th of April, the monthly aggregated data is already available for March 2020. For details, see here

The focus of this exercise is the Belgian waters (the Belgian Exclusive Economic Zone) and the Western Scheldt

We extract the Vessel density data, for example here for the Fishing map of January 2019:

Belgian EEZ:

We extract all data from all the cells, and calculate the average for the whole BCP.

This is an example of the data in ‘wide’ format

A plot of the result:

Scheldt estuary:

A plot of the result:

2020 vs 2019

We create a mean value for

  • February - April 2019
  • February - April 2020

And plot both periods rasters (the map is the Fishing type example)

Cargo

Fishing

Passenger

Tanker

Other

All

Anomaly maps

February - April 2019 compared to February - April 2020

Comparing the period February-April 2020 vs 2019: (the different layers are the different boat types)

Month-by-month comparisons 2019 vs 2020

Comparing the month-by-month differences between 2020 vs 2019: (the map is the Fishing type example)

Cargo

Fishing

Passenger

Tanker

Other

All

Additional analyses

Passengers excluding wind farm

There is quite some traffic to the windfarms that are classified as ‘Passenger’ traffic. As this might be confusing, we will exclude this from the analysis.

Fishing zones.

In this part we’ll look how much is being fished in the different zones:

Data availability

  • Vessel densities The data from this exercise is freely available at the EMODnet Human activities portal. EMODnet stands for the European Marine Observation and Data Network and is a network of organisations that are collecting and standardizing European marine data, and making those data products freely available, supported by EU’s integrated marine policy. The vessel densities data used in this exercise are provided by the European Maritime Safety Agency (EMSA) to EMODnet human activities and are available here.

  • Maritime boundaries The maritime boundaries used in this exercise are from MarineRegions.org. MarineRegions.org maintains a standard, relational list of geographic names coupled with information and maps of the geographic location of these features. This improves access and clarity of the different geographic, marine names and allows an improved linking of these locations to databases. Marine Regions is developed by Flanders Marine Institute (VLIZ) as part of the Flemish contribution to LifeWatch, funded by Research Foundation - Flanders. The more information about the polygons used in this exercise:

Code availability

All the code needed to run this analysis is available here.

This code makes use of following R packages:

  • raster: for raster data
  • sf: for spatial data
  • mapview: for interactive maps
  • ggplot2: for plots
  • data.table: for manipulation dataframes/tables
  • DT: for visualisation of the data tables
  • mregions: for standardize marine regions from http://www.marineregions.org